class_docstr: Implement Alpha Suffix model averaging.
  This method is based on paper "Making Gradient Descent Optimalfor Strongly
  Convex Stochastic Optimization" (https://arxiv.org/pdf/1109.5647.pdf).
docstr: Parameters
  ----------
  metric
      The criteria to trigger averaging, not used in Alpha Suffix.
  epoch
      The epoch to start averaging.
  
  Returns
  -------
docstr_tok: ["Parameters","--","--","--","--","--","metric","The","criteria","to","trigger","averaging",",","not","used","in","Alpha","Suffix",".","epoch","The","epoch","to","start","averaging",".","Returns","--","--","--","-"]
code: def update_average_trigger(
      self, metric: Any = None, epoch: int = 0, **kwargs
  ):
      r"""
      Parameters
      ----------
      metric
          The criteria to trigger averaging, not used in Alpha Suffix.
      epoch
          The epoch to start averaging.
  
      Returns
      -------
      """
  
      if not self.averaging_started:
          if epoch >= self.alpha_suffix:
              self.averaging_started = True
  
imports: from typing import Any, Dict, List, Optional
  import logging
  import mxnet
  import mxnet.gluon.nn
  from mxnet import gluon
  from gluonts.core.component import validated
  from .callback import Callback
label_desc: Use image node_id 2 for calling the Alpha_Suffix obj's underlying member method code with example usage: obj.update_average_trigger(metric, epoch) without return types